Network and discrete location : models, algorithms, and applications / Mark S. Daskin, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI.

Includes bibliographical references and index.

Print version record and CIP data provided by publisher.

Network and Discrete Location: Models, Algorithms, and Applications; Contents; Preface to the First and Second Editions; Acknowledgments; 1. Introduction to Location Theory and Models; 1.1 Introduction; 1.2 Key Questions Addressed by Location Models; 1.3 Example Problem Descriptions; 1.3.1 Ambulance Location; 1.3.2 Siting Landfills for Hazardous Wastes; 1.3.3 Summary; 1.4 Key Dimensions of Location Problems and Models; 1.4.1 Planar Versus Network Versus Discrete Location Models; 1.4.2 Tree Problems Versus General Graph Problems; 1.4.3 Distance Metrics; 1.4.4 Number of Facilities to Locate.

1.4.5 Static Versus Dynamic Location Problems1.4.6 Deterministic Versus Probabilistic Models; 1.4.7 Single- Versus Multiple-Product Models; 1.4.8 Private Versus Public Sector Problems; 1.4.9 Single- Versus Multiple-Objective Problems and Models; 1.4.10 Elastic Versus Inelastic Demand; 1.4.11 Capacitated Versus Uncapacitated Facilities; 1.4.12 Nearest Facility Versus General Demand Allocation Models; 1.4.13 Hierarchical Versus Single-Level Models; 1.4.14 Desirable Versus Undesirable Facilities; 1.5 ATaxonomy of Location Models; 1.5.1 Typology of Location Models; 1.5.2 A Simple Analytic Model.

1.6 SummaryExercises; 2. Review of Linear Programming; 2.1 Introduction; 2.2 The Canonical Form of a Linear Programming Problem; 2.3 Constructing the Dual of an LP Problem; 2.4 Complementary Slackness and the Relationships Between the Primal and the Dual Linear Programming Problems; 2.5 Solving a Linear Programming Problem in Excel; 2.6 The Transportation Problem; 2.7 The Shortest Path Problem; 2.7.1 The Shortest Path Problem in Excel; 2.7.2 The Shortest Path Problem in AMPL; 2.8 The Out-of-Kilter Flow Algorithm; 2.9 Integer Programming Problems; 2.10 Summary; Exercises.

3. An Overview of Complexity Analysis3.1 Introduction; 3.2 Basic Concepts and Notation; 3.3 Example Computation of an Algorithm's Complexity; 3.4 The Classes P and NP (and NP-Hard and NP-Complete); 3.5 Summary; Exercises; 4. Covering Problems; 4.1 Introduction and the Notion of Coverage; 4.2 The Set Covering Model; 4.3 Applications of the Set Covering Model; 4.4 Variants of the Set Covering Location Model; 4.5 The Maximum Covering Location Model; 4.5.1 The Greedy Adding Algorithm: A Heuristic Algorithm for Solving the Maximum Covering Location Model.

4.5.2 Lagrangian Relaxation: An Optimization-Based Heuristic Algorithm for Solving the Maximum Covering Location Model4.5.3 Other Solution Approaches and Example Results; 4.6 An Interesting Model Property or It Ain't Necessarily So; 4.7 The Maximum Expected Covering Location Model; 4.8 Summary; Exercises; 5. Center Problems; 5.1 Introduction; 5.2 Vertex P-Center Formulation; 5.3 The Absolute 1- and 2-Center Problems on a Tree; 5.3.1 Absolute 1-Center on an Unweighted Tree; 5.3.2 Absolute 2-Centers on an Unweighted Tree; 5.3.3 Absolute 1-Center on a Weighted Tree.

This Second Edition remains the only hands-on guide to using and developing facility location models. It offers a practice-oriented introduction to model-building methods and solution algorithms complete with software for solving classical problems of realistic size and end-of-chapter exercises to enhance reader understanding. The book introduces readers to the key classical location problems (covering, center, median, and fixed charge); discusses real-world extensions of the basic models used in locating; outlines a host of methodological tools for solving location models; and much more.